000192398 001__ 192398
000192398 005__ 20190316235758.0
000192398 0247_ $$2doi$$a10.1109/TIP.2012.2210238
000192398 037__ $$aARTICLE
000192398 245__ $$aA Track Creation and Deletion Framework for Long-Term Online Multi-Face Tracking
000192398 269__ $$a2013
000192398 260__ $$c2013
000192398 336__ $$aJournal Articles
000192398 520__ $$aTo improve visual tracking, a large number of papers study more powerful features, or better cue fusion mechanisms, such as adaptation or contextual models. A complementary approach consists of improving the track management, that is, deciding when to add a target or stop its tracking, for example, in case of failure. This is an essential component for effective multiobject tracking applications, and is often not trivial. Deciding whether or not to stop a track is a compromise between avoiding erroneous early stopping while tracking is fine, and erroneous continuation of tracking when there is an actual failure. This decision process, very rarely addressed in the literature, is difficult due to object detector deficiencies or observation models that are insufficient to describe the full variability of tracked objects and deliver reliable likelihood (tracking) information. This paper addresses the track management issue and presents a real-time online multiface tracking algorithm that effectively deals with the above difficulties. The tracking itself is formulated in a multiobject state-space Bayesian filtering framework solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step decides when to add or remove a target from the tracker, where decisions rely on multiple cues such as face detections, likelihood measures, long-term observations, and track state characteristics. The method has been applied to three challenging data sets of more than 9 h in total, and demonstrate a significant performance increase compared to more traditional approaches (Markov Chain Monte Carlo, reversible-jump Markov Chain Monte Carlo) only relying on head detection and likelihood for track management.
000192398 700__ $$aDuffner, Stefan
000192398 700__ $$0243995$$aOdobez, Jean-Marc$$g161663
000192398 773__ $$j22$$k1$$q272-285$$tIEEE Transactions on Image Processing
000192398 8564_ $$s1388126$$uhttps://infoscience.epfl.ch/record/192398/files/Duffner_IEEE-IP_2012.pdf$$yn/a$$zn/a
000192398 909C0 $$0252189$$pLIDIAP$$xU10381
000192398 909CO $$ooai:infoscience.tind.io:192398$$pSTI$$particle$$qGLOBAL_SET
000192398 917Z8 $$x148230
000192398 937__ $$aEPFL-ARTICLE-192398
000192398 970__ $$aDuffner_IEEE-IP_2012/LIDIAP
000192398 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000192398 980__ $$aARTICLE